An article focused on deep diving into observability and its significance in software. Its history, goals, the importance of observability, and the issues that may occur if not present in the software life-cycle.
In this article, we’ll take a deep dive into observability and its significance in software’s. We would learn about its history, goals, and the importance of observability, as well as the issues that may occur if it is not present in the software life-cycle.
We would also analyze the key distinctions between observability and monitoring. Lastly, we will look at best practices for adopting observability, factors to consider when selecting an observability tool, and how to adopt the best strategy for your business.
The term “observability” is derived from the word “observe,” which implies attentively observing something with the aim of reaching a conclusion.
Coming back to the technical domain, observability was coined by Rudolf E. Kálmán In 1960, and has its roots tied to Control Theory (a branch of Applied Mathematics dealing with the use of feedback to influence the behavior of a system in order to achieve the desired goal), In mechanical systems, sensors and detectors measure the output to inform controls that are in place.
Observability is simply knowing what your users, systems, and applications are doing at any given time (data gathering) to inform you what is wrong and why it happened (tracing).
We can’t fix something which we can’t observe
Observability’s first appearance, One of the term’s first appearances was in 2013 when the engineers at Twitter, published a blog post called “Observability at Twitter” which was instituted to observe the health and performance of the “diverse service topology” as they moved from a monolithic to a distributed IT architecture.
Now that we’ve covered the background, let’s look at the fundamental distinctions between “observability” and “monitoring,” as the English language interprets them to mean almost the same thing.
Monitoring simply tells and shows you that something is wrong as it is based on gathering predefined sets of metrics or logs in a system, while Observability uses data collection to tell you what is wrong and why it happened to enable SRE (system reliability engineers) or DevOps team easily debug their system, so it is based on exploring metrics and patterns that may have not been defined in advance.
Starting with problems reported by research from the State of Observability 2021 report:
Which then led to a significant percentage of respondents severely paying for the consequences of service failures with:
Observation is surely not disregarded in legal and compliance departments, as it plays a significant role in ensuring organizations comply with their legal obligation to secure sensitive data from unauthorized access. Observability technology may also be used in security to identify breaches and intrusions and prevent data leaks, allowing governments and regulatory agencies to avoid or reduce fines for noncompliance.
Statistics also continue to show that organizations with observability practices have seen revenue growth and profitability due to the analytical insight that can be monitored from customer behaviors, which helps marketers make strategic decisions and also improve the user experience, which then boosts the organization’s reputation.
So, what's next?
Metrics are the numeric representations of how data is measured over intervals in time. Everything from operating systems to apps generates metrics that include a set of attributes like name, a time stamp, and a field to indicate some value typically conveying information about the SLAs, SLOs, and SLIs.
When it comes to monitoring, metrics are a logical place to start because so many resources come ready to alert us about themselves. Typically, SREs and ops engineers use metrics to trigger alerts whenever a system value goes above a specified threshold and these metrics are selected key performance indicators (KPIs) such as response time, peak load, requests served, CPU capacity, memory usage, error rates, and latency. sample KPIs:
Logging is a very important process in the monitoring toolbox because virtually everything logs information about what they are doing at any given time. Furthermore, logs provide more detailed information about resources than metrics, and most application frameworks, libraries, and languages come with support for logging. So, if metrics revealed that the resource was dead, logs will assist you to figure out why it died.
The problem with logs is there can be too much of a good thing. With everything in your environment tracking what they are doing and anxious to share that information, it is easy to see how that could result in a heavy ton of data, and instead of simplifying the monitoring process, you might just be creating another big new centralized haystack.
Logs and metrics are useful for evaluating individual system behavior and performance, but they are rarely useful for determining the lifecycle of a request in a distributed system. Tracing is another observability approach that allows you to see and understand the whole lifetime of an action across several systems.
A trace, as explained above, can be said to be a representation of a series of causally associated distributed events that encapsulate the end-to-end request flow through a distributed system.
A trace reflects the whole path of a request or action, providing critical visibility into a system’s health as it traverses all nodes in a distributed system. Traces provide for system profiling and inspection, notably for containerized applications, serverless architectures, and microservices architectures.
By analyzing trace data, you would monitor overall system health, identify potential problems, discover and handle issues more quickly, and identify high-value areas for optimization and improvements.
When thinking of being able to optimize for success in your organization, customer experience is most times the first thing to consider and a key to the success of most tech companies today. Another critical criterion is the necessity to proactively resolve things such as application availability and performance in production. This is where observability comes into play. A solid observability architecture is now required to keep your systems running smoothly.
I’ve compiled a list of observability tools for log aggregation, distributed tracing, APM, time-series analysis, and metrics collection. Although, is not based on an in-depth examination of the key benefits or comparisons of the tools; however, it is an excellent beginning place to get started on your road to increased observability.
By selecting the right observability platform, getting a connected view of your system’s performance data in a singular view, and establishing an observability culture, you’ll be able to identify issues faster, understand what caused them, and eventually, build customer-focused products at greater speeds.
Some of the Top observability and log management tools to consider.
Then, you need to identify and monitor metrics related to issues you’ve already experienced and those you could likely encounter in the future.
After incorporating observability into your incident management process, you must identify and monitor metrics linked to issues that have already occurred and those that are expected to occur in the future. Afterward, you should establish an observability-centric culture throughout your organization.
Without implementing this model, you may not be able to get the most out of your observability architecture since no amount of observability tooling can replace solid engineering instincts and intuition.